Large-Sample Hydrology to Improve Prediction

in Ungauged Basin using Information Theory

Techniques

Surface Hydrology and Watershed Management (TS8)

20th December 2024, 11:55 AM to 01:00 PM (Venue: CT Hall )

HYDRO 2024 INTERNATIONAL

29th International Conference on Hydraulics, Water Resources, River & Coastal Engineering

Central Water &Power Research Station, Pune, India

Dr. Ankit Deshmukh

School of Technology, PDEU.

ankit.Deshmukh@sot.pdpu.ac.in

ankitdeshmukh.com | anixn

I will be talking about

Prediction in Ungauged Basins (PUB)

Treading space for time approach

Large sample hydrology with TSFT

Catchment similarity for PUB

Hydrologic modeling is channeling in data scares region

•Majority of the Basin/catchments in India are ungauged or the availability of the streamflow is not

suitable for reliable streamflow projection [Kumar et al., 2018; Sivapalan et al., 2017].

•Climate change adding further projection uncertainty [Adler et al., 2003, Brohanetal 2006]

Prediction in Ungauged Basins (PUB)

•Prediction in the ungauged basin is a paramount problem in water resource management as

observed discharge is a key parameter of all the hydrological paradigm.

•PUB refers to estimating hydrological behaviors (streamflow and runoff), in river basins where

direct measurements are unavailable.

•Several approaches are produced in the predicting runoff in ungauged basin or provide a

workaround [Sivapalan, M. (2003)].

•Problem still persist due to lack of data availability, Regionalization & Transferability, Modeling

Complexities, Climate Change Impacts [Blöschl et al., 2013; Hrachowitz et al., 2013, Feng et al.,

2020]

Best practices for predictions in ungauged basins

by Takeuchi et al. (2013) in Blöschl et al. (2013)

Trading Space for Time (TSFT)

One of the recently established approaches is trading space for time [TSFT] . [Singh et.al, 2011 ,

Deshmukh and singh, 2016, 2019;].

Where we utilize large number of catchment information to overcome the low availability of other

parameters or discharge data.

Several climate scenario are used to explore the possible unprecedented changes in the future.

A trading-space-for-time approach to probabilistic continuous streamflow

predictions in a changing climate—accounting for changing watershed behaviour.

Singh et.al, 2011

Deshmukh and singh, 2019

Deshmukh and singh, 2016

Large sample hydrology (LHS) transferable hydrological models

- To deal with PUB problem we can leverages extensive datasets across a wide range of spatial and

temporal scales to uncover patterns.

- improving regionalization techniques for ungauged basins.

- advancing hydrological models to handle climate change induced extreme events. [Vogel et al.,

2003, Addor et al., 2017]

Recently available vast number of discharge data and catchment attribute data (CAMELS) allow us to

for testing the hypothesis on TSFT.

Average flow

condition

Low flow

conditions

High flow

conditions

Magnitude of flow events

46 22 27

Timing of flow events

03 03 03

Rate of change in flow events

09 00 00

Frequency of flow events

00 03 11

Duration of flow events

00 20 24

Olden, J. D., & Poff, N. L. (2003)

Using LSH to determined the similarity in the catchment groups

Recently availability vast number of discharge data and catchment attribute data (CAMELS) provides

useful link to relate the catchments

It is helps for Indian catchment where a link can be generated between ungauged and gauged

catchment based on similar grouping/ clusters of catchments.

We provide a simple framework to assess the similarity in the catchments:

Study area and data

CARAVAN1give access of more then

6000 catchments2.

Caravan - A global community dataset

for large-sample hydrology [Kratzert et

al., 2024]

We select 4 country for this study and

choose 50 catchment from each based

on the catchment area size [500-

2500km2]

___

1: Catchment Attributes and MEteorology for

Large sample Studies

1: 482 CAMELS1(US) , 150 CAMELS-AUS, 376

CAMELS-BR, 314 CAMELS-CL, 408 CAMELS-GB,

4621 HYSETS, 479 LamaH-CE

Study area and data for analysis

Methodology

We select 4 country (Australia, Chile, India, USA) for this study and choose 50 catchment from each

based on the catchment area size [500-2500km2]

Allow to find the similar catchment base on hydrologically similarity

•Divide the catchments in characteristic into 3 groups Grouping PA, CA, HA

•Find spatial similarity based on clustering (elbow method: 5 clusters for all the grouping.)

Hopkins statistic is used to find the cluster tendency of the data

•The Hopkins statistic is a way of measuring the cluster tendency of a data set.

•Computed Hopkins statistic for random data 0.5. With the data we have it near 1 (Clusterable) [Hopking et al.,

1954]

Cluster plot for CA and PA groups.

The clustering of the CA and PA grouping is shown in the figure. An ideal number of clusters (n = 5) are found using

gap statistics and elbow method.

Evaluate cluster with variable gourping

•Internal similarity criteria: to attain high intra-cluster and low inter-cluster similarity.

•External criteria of clustering quality:

•Rand Index

•Purity

•F-measure

•Normalize Mutual Information (NMI)

Normalize Mutual Information: NMI compares how much

information random variables (Cluster Vectors) share.

Entropy in information theory is a measure of uncertainty

or randomness in a set of data.

I: is mutual information

H: is entropy

Image Source: https://www.pngaaa.com/download/1559450

Normalized mutual information and correlation between CA, PA, and HI.

Several combination of CA PA HI will be created and optimize with highest NMI values. One of set is

show in the table below.

We can conclude with the above table that CA and HI grouping 25% explains each other, similarly, this number

is 40% for PA and HI grouping. We found strong location bias in the clustering

of the catchment.

Thank you!

Questions?

www. ankitdeshmukh.com

About my research

oMy fields of interest are:

oComputational Hydrology,

oWater resource management

oUnderstating the catchment response under

anthropogenic changes.

My specialization is on: “The approaches to identify the

catchment vulnerability to environmental changes.”

My current research focuses on the development of a

Physio-climatic catchment characteristics dataset for

the Indian subcontinent that can be utilized for

prediction in the ungauged basins. I possess a strong

understanding of GIS processing and am efficient in

Geo-spatial analysis.

I am highly motivated in the field of data analysis

(finding meaningful insights in data and ML), skilled in

programming with R, Python, and SQL scripting.

Reach out to me:

Dr. Ankit Deshmukh, D-block

PDEU C7 2nd Floor, Gandhinagar

/ankit-deshmukh-2

/anix7n

/anixn

Reference

•Sivapalan, M. (2003). "Prediction in Ungauged Basins: A Grand Challenge for Theoretical Hydrology." Hydrological Processes, 17(15), 3163-3170.

•Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., & Savenije, H. (Eds.). (2013). Runoff Prediction in Ungauged Basins: Synthesis Across Processes, Places, and Scales.

Cambridge University Press.

•Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., ... & Wagener, T. (2013). "A Decade of Predictions in Ungauged Basins

(PUB)—A Review." Hydrological Sciences Journal, 58(6), 1198-1255.

•Feng, D., Lawson, K., & Shen, C. (2020). "Prediction in Ungauged Regions with Sparse Flow Duration Curves and Input-Selection Ensemble Modeling." arXiv preprint

arXiv:2011.13380.

•Willard, J. D., Varadharajan, C., Jia, X., & Kumar, V. (2023). "Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water

Resources." arXiv preprint arXiv:2308.09766.

•Kratzert, F., et al. Caravan - A Global Community Dataset for Large-sample Hydrology. 1.4, Zenodo, 16 Apr. 2024, doi:10.5281/zenodo.10968468.

•Vogel, R. M., & Sankarasubramanian, A. (2003). "Validation of a Watershed Model Without Calibration." Water Resources Research, 39(10), 1-14.

•Addor, N., Newman, A. J., Mizukami, N., & Clark, M. P. (2017). "The CAMELS Data Set: Catchment Attributes and Meteorology for Large-Sample Studies." Hydrology and

Earth System Sciences, 21(10), 5293–5313.

•Olden, J. D., & Poff, N. L. (2003). Redundancy and the choice of hydrologic indices for characterizing streamflow regimes. River Research and Applications, 19(2), 101–

121. https://doi.org/10.1002/rra.700

•Hopkins, Big D Randy; Skellam, Harry Kimmel I Gordon (1954). "A new method for determining the type of distribution of plant individuals". Annals of Botany.18 (2). Annals

Botany Co: 213–227. doi:10.1093/oxfordjournals.aob.a083391.

•Kumar, S., & Kaur, R. (2018). Hydrological Modeling of the Ganga Basin. In S. Kumar et al. (Eds.), Hydrology in Practice. Springer.

DOI: 10.1007/978-3-319-91548-6

•Sivapalan, M., & Blöschl, G. (2017). The growth of hydrological understanding: Technologies, ideas, and societal needs shape the field. Water Resources Research,

53(10), 8137–8146. DOI: 10.1002/2017WR021396